store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_445055', 'seed': 445055, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[6967, 56469, 56851, 27439, 52408, 31658, 48396, 53630, 54187, 38654, 56748, 58713, 42541, 40538, 40114, 23932, 31, 6606, 41924, 25562]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.5635, 'nll': 3.616140007019043}, 'chosen_samples': [29320, 6214, 34815, 19793, 39407, 9318, 16298, 50105, 33239, 33233, 21330, 15614, 9774, 52899, 21425, 36159, 54058, 11033, 5685, 25710, 37167, 16416, 32352, 32135, 23117, 57355, 12481, 42805, 42596, 32763, 46200, 24841, 34770, 59355, 7614, 27935, 42117, 38288, 38593, 27897], 'chosen_samples_score': ['1.1312108', '1.1317663', '1.1326318', '1.1328106', '1.1457523', '1.14305', '1.1394118', '1.1418817', '1.1462481', '1.1482339', '1.1373065', '1.1452848', '1.1489015', '1.3472993', '1.1773067', '1.1931152', '1.1828616', '1.1607671', '1.1795557', '1.1610746', '1.24164', '1.1739085', '1.1730332', '1.1575075', '1.1925166', '1.1628513', '1.2522348', '1.2579577', '1.1564286', '1.1982445', '1.2473681', '1.1555748', '1.1710584', '1.2081012', '1.1698081', '1.2416596', '1.1525524', '1.1987925', '1.227936', '1.2194667']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.5887, 'nll': 2.3618649147033692}, 'chosen_samples': [1208, 14888, 23111, 9852, 11494, 47368, 44348, 40764, 56184, 25493, 46362, 56627, 23042, 47615, 11191, 6724, 49992, 120, 22683, 33759, 21452, 2650, 51492, 53172, 27926, 51584, 28424, 2859, 1622, 28398, 44706, 49828, 21819, 29369, 21935, 11087, 52148, 9026, 48033, 2662], 'chosen_samples_score': ['0.888706', '0.89161617', '0.8959495', '0.89721256', '0.9223127', '0.91055804', '0.91842103', '0.9194533', '0.90865445', '0.9182862', '0.91035986', '0.90227765', '0.9310796', '0.91284966', '0.91359', '0.9129322', '0.903462', '0.9193221', '0.92342097', '0.9134744', '0.91061914', '0.9034167', '0.9161619', '0.89961106', '0.9261189', '0.89987403', '0.9344473', '0.96750176', '0.9620573', '0.96338373', '0.9637581', '0.9579678', '0.96347165', '0.95806575', '0.9652854', '0.93712324', '0.93717766', '0.9611896', '0.9442938', '0.96454424']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7585, 'nll': 1.2429268772125244}, 'chosen_samples': [31904, 2579, 12180, 59894, 40390, 45942, 12607, 12157, 4773, 10732, 57186, 55721, 14484, 17784, 27082, 43610, 7643, 1940, 19915, 52635, 27401, 27257, 48790, 25046, 25662, 45845, 47613, 48668, 23041, 20238, 50500, 20139, 53977, 4723, 3128, 45728, 43163, 551, 11789, 4226], 'chosen_samples_score': ['0.7702726', '0.7724855', '0.7768016', '0.7719882', '0.77825356', '0.7783976', '0.7786715', '0.7802104', '0.780523', '0.7821348', '0.7835284', '0.79725933', '0.79745686', '0.78397757', '0.7920397', '0.7949114', '0.78593624', '0.79103553', '0.78526294', '0.7897518', '0.7968334', '0.80556035', '0.8130374', '0.8417864', '0.83467925', '0.8086317', '0.85633886', '0.8222661', '0.81528497', '0.8443832', '0.84022737', '0.84238124', '0.8117643', '0.8160598', '0.82924587', '0.80841047', '0.8733947', '0.8313253', '0.9055878', '0.8240452']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8182, 'nll': 0.9700147569656372}, 'chosen_samples': [12463, 47132, 45536, 14935, 20476, 40589, 5887, 41233, 28477, 46453, 45975, 53740, 6327, 15140, 47651, 56782, 59681, 57837, 52086, 43588, 24426, 57090, 11947, 47858, 17756, 37588, 7851, 14063, 33254, 21532, 39380, 26290, 41557, 26676, 42878, 30141, 16692, 34039, 37086, 48237], 'chosen_samples_score': ['0.8660414', '0.86664504', '0.8797634', '0.86794055', '0.8756743', '0.88083816', '0.8812524', '0.881448', '0.9083707', '0.90589875', '0.9177168', '0.9727163', '0.9412506', '0.9478378', '0.97381824', '0.9339419', '0.98542947', '0.9091756', '0.9028716', '0.89305294', '0.8958689', '0.9426551', '0.8845504', '0.9048084', '0.90102357', '0.91116005', '0.886015', '0.8909068', '0.9541107', '0.8978988', '0.92211384', '0.94520766', '0.9403567', '1.0383492', '0.9137701', '0.9146142', '0.9126561', '0.8868201', '0.88560164', '0.9437156']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8595, 'nll': 0.773678044128418}, 'chosen_samples': [40125, 20903, 13030, 29595, 4421, 21395, 36337, 3051, 58560, 37720, 16072, 26460, 12305, 11167, 38607, 21315, 9538, 30103, 57211, 11586, 11619, 7033, 33836, 33095, 36281, 42763, 30188, 59468, 50957, 17457, 37315, 41018, 53656, 52151, 31736, 13073, 42477, 10244, 36268, 20169], 'chosen_samples_score': ['0.75308335', '0.7551317', '0.76077956', '0.8246001', '0.78911287', '0.7928644', '0.77282834', '0.7808061', '0.77021635', '0.79239684', '0.7580515', '0.75756806', '0.822623', '0.7903742', '0.80317336', '0.7675067', '0.81826633', '0.78838223', '0.79863435', '0.81219435', '0.760813', '0.7588009', '0.77886134', '0.7959711', '0.7943384', '0.7979954', '0.8165715', '0.79046106', '0.7651423', '0.7840461', '0.8421508', '0.80526346', '0.78204465', '0.7603692', '0.8085119', '0.8209019', '0.8497885', '0.85287243', '1.0267167', '0.91671634']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9165, 'nll': 0.5279745242118835}, 'chosen_samples': [51508, 26444, 25646, 37339, 13752, 8821, 51464, 58470, 45073, 28512, 8678, 41108, 27898, 32427, 31356, 28930, 29786, 26072, 19194, 55090, 19942, 44629, 3070, 14286, 49537, 59314, 57708, 18473, 23369, 35064, 13983, 15679, 42703, 37048, 12514, 21040, 26693, 39307, 26850, 30884], 'chosen_samples_score': ['0.84875005', '0.8509628', '0.8517884', '0.8521075', '0.85434854', '0.8532191', '0.85482883', '0.85668916', '0.8532207', '0.8549146', '0.8564361', '0.8569802', '0.8583684', '0.9756631', '0.9046895', '1.0107472', '0.93370706', '0.9098925', '0.91152376', '0.8863431', '0.88864017', '0.87124395', '0.85852015', '0.8771008', '0.9036088', '0.91245973', '0.9881191', '0.99106926', '0.9333536', '0.8918453', '1.0648193', '0.88402194', '0.8598166', '0.93642324', '0.8675872', '0.9130102', '0.86238366', '0.8577313', '1.0077556', '0.86199373']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9348, 'nll': 0.45304347896575925}, 'chosen_samples': [40964, 11749, 14290, 29180, 6149, 57882, 38050, 4058, 9588, 40022, 17159, 34520, 14681, 29493, 8849, 7596, 23802, 31312, 36417, 24473, 6474, 35938, 52089, 56472, 36363, 37702, 52800, 22772, 51004, 14896, 13526, 29508, 19590, 5308, 44255, 27194, 26150, 31557, 29130, 39864], 'chosen_samples_score': ['0.88953', '0.8910471', '0.8915789', '0.89405775', '0.89164305', '0.8950375', '0.89524', '0.90912414', '0.897052', '0.90143245', '0.9053154', '0.8967912', '0.9054831', '0.9091526', '0.91240984', '0.9240181', '0.9248471', '0.91676384', '0.9228843', '0.9165853', '0.93012935', '0.9131741', '0.91701657', '0.94203097', '1.0185661', '0.96025753', '0.9725264', '1.1469514', '0.9545836', '1.0067027', '0.95601815', '1.0044153', '1.0769789', '1.0157535', '1.045789', '0.96829444', '0.95995253', '0.9824845', '0.9462968', '0.9646008']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.938, 'nll': 0.4272736569404602}, 'chosen_samples': [29899, 47484, 966, 31624, 40828, 14355, 6755, 4165, 3676, 49333, 10257, 45848, 23059, 30844, 7833, 13149, 54161, 39668, 8892, 17540, 2092, 43126, 57276, 13099, 44131, 33812, 22505, 45502, 53872, 41567, 6347, 50370, 34743, 5684, 52097, 21692, 15973, 40184, 52753, 44286], 'chosen_samples_score': ['0.88121337', '0.88239634', '0.8843706', '0.88795936', '0.88385266', '0.88839483', '0.9281576', '0.9470776', '0.8912994', '0.93536955', '0.8906002', '0.90105903', '0.92941153', '0.97391355', '0.94999146', '0.9960989', '0.90372413', '0.9228855', '1.0176251', '0.90053195', '0.92259175', '1.0202924', '0.9444312', '0.9158572', '0.92508334', '0.9491367', '0.92916745', '0.9925899', '0.96443653', '0.9070942', '0.9036469', '0.90129226', '0.9715457', '0.90902495', '0.9011738', '0.91192836', '0.9423141', '0.9780075', '1.0366504', '0.9513103']})
store['iterations'].append({'num_epochs': 9, 'test_metrics': {'accuracy': 0.9483, 'nll': 0.4043622099876404}, 'chosen_samples': [31941, 39151, 45752, 17684, 31664, 32343, 10746, 13969, 21023, 42317, 47540, 47340, 52875, 20221, 39561, 53106, 28631, 21918, 25351, 53280, 34328, 30123, 4153, 13705, 41307, 212, 45462, 39453, 25246, 58390, 24479, 35068, 10114, 9472, 22561, 37469, 31885, 49493, 54570, 32355], 'chosen_samples_score': ['0.9024568', '0.9038184', '0.90462583', '0.9061501', '0.9064808', '0.9187232', '0.9090378', '0.9173402', '0.92506593', '0.9316152', '0.92676884', '0.929154', '0.93208337', '0.9346978', '0.9361214', '0.9294013', '0.9375914', '0.9924713', '0.946049', '0.9684287', '0.94286203', '0.97260463', '0.9763015', '0.9913765', '0.97513753', '0.9398703', '0.9777741', '0.96115506', '0.94025755', '0.9591431', '0.99503887', '0.9540711', '0.9960368', '1.1643102', '1.074827', '1.0098011', '1.1212628', '1.0163304', '1.0223203', '1.0035863']})
store['iterations'].append({'num_epochs': 16, 'test_metrics': {'accuracy': 0.9623, 'nll': 0.3309257441520691}, 'chosen_samples': [15766, 5536, 52690, 21495, 50317, 55856, 33318, 15450, 33340, 17365, 52074, 8932, 16816, 59747, 14619, 31345, 22272, 47548, 9118, 53854, 46658, 9557, 53990, 56464, 59731, 1518, 10884, 29759, 25330, 9147, 18738, 41196, 1674, 15771, 8118, 14715, 42384, 36126, 57862, 8443], 'chosen_samples_score': ['0.9781213', '0.98548603', '0.9784188', '0.98429483', '0.98662984', '0.984952', '0.9851282', '0.9817598', '0.9808088', '0.9868825', '0.9898341', '1.0024168', '0.99001646', '1.0048459', '1.0508457', '1.0251604', '1.0281136', '1.0648017', '0.99582344', '1.030869', '1.1300154', '1.0412412', '0.9882498', '0.98936236', '1.0180271', '1.0344911', '0.99141204', '1.0616', '0.9946656', '1.0541636', '1.0621989', '1.0107269', '1.0543444', '0.9970371', '1.0141532', '1.0039748', '1.0106573', '1.1130704', '1.105529', '1.0274103']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9613, 'nll': 0.3390408893585205}, 'chosen_samples': [11044, 58832, 54280, 14735, 7612, 29744, 28988, 49161, 31197, 58040, 33391, 31046, 57728, 10086, 23812, 33426, 44590, 30147, 4652, 31184, 22994, 22607, 26358, 49487, 2845, 2426, 19243, 7058, 21335, 27793, 11292, 49525, 26722, 47220, 23730, 48507, 53156, 55739, 48397, 20037], 'chosen_samples_score': ['0.8177567', '0.8218866', '0.82237256', '0.82242304', '0.8238032', '0.8242559', '0.8271833', '0.8251742', '0.8283648', '0.82439506', '0.8284033', '0.82883704', '0.8700347', '0.8338065', '0.848872', '0.8544438', '0.850729', '0.83696973', '0.85548097', '0.87231976', '0.85598', '0.8748636', '0.83246744', '0.8300378', '0.87629515', '0.8334536', '0.84035057', '0.8817683', '0.92613614', '0.9397559', '0.9303206', '0.93826616', '0.8839307', '0.90667176', '0.88332695', '0.93709177', '0.9005612', '0.95314777', '0.9004959', '0.9322261']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9638, 'nll': 0.30001427426338195}, 'chosen_samples': [18096, 28357, 39405, 49354, 1682, 13942, 18864, 34665, 8584, 46368, 9948, 4955, 32276, 5013, 47154, 17121, 32850, 41933, 20150, 22139, 51180, 3719, 56514, 19616, 30900, 20641, 7168, 2803, 31252, 6044, 9567, 20859, 15948, 42428, 23086, 36434, 24078, 35632, 34946, 14722], 'chosen_samples_score': ['0.84993684', '0.85123754', '0.8550397', '0.9059732', '0.8908112', '0.9778946', '0.95540994', '1.0236052', '0.91801256', '0.93605345', '0.93856245', '0.9091179', '1.0605297', '0.8574259', '0.8898014', '0.93917257', '1.0172544', '0.89310384', '1.0154781', '0.8761716', '0.89088035', '0.9998449', '0.95551187', '0.85761195', '0.9757516', '0.90854084', '0.9656933', '0.8694737', '0.8579462', '0.8969613', '0.89937407', '0.8562863', '0.85564226', '0.90409356', '0.8640256', '0.8665836', '0.8581743', '0.9136586', '0.8999813', '0.91676366']})
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